Rearte Ramiro, Lorenti Santiago Nicolas, Dominguez German, de la Sota Rodolfo Luzbel, Lacau-Mengido Isabel María, Giuliodori Mauricio Javier
Cátedra de Higiene, Epidemiología y Salud Pública, Facultad de Ciencias Veterinarias-Universidad Nacional de La Plata (FCV-UNLP), La Plata B1900AVW, Argentina.
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires C1033AAJ, Argentina.
Animals (Basel). 2023 Oct 6;13(19):3114. doi: 10.3390/ani13193114.
A retrospective longitudinal study assessing the explanatory and predictive capacity of body condition score (BCS) in dairy cows on disease risk at the individual and herd level was carried out. Data from two commercial grazing herds from the Argentinean Pampa were gathered (Herd A = 2100 and herd B = 2600 milking cows per year) for 4 years. Logistic models were used to assess the association of BCS indicators with the odds for anestrus at the cow and herd level. Population attributable fraction (AF) was estimated to assess the anestrus rate due to BCS indicators. We found that anestrus risk decreased in cows calving with BCS ≥ 3 and losing ≤ 0.5 (OR: 0.07-0.41), and that anestrus rate decreased in cohorts with a high frequency of cows with proper BCS (OR: 0.22-0.45). Despite aggregated data having a good explanatory power, their predictive capacity for anestrus rate at the herd level is poor (AUC: 0.574-0.679). The AF varied along the study in both herds and tended to decrease every time the anestrous rate peaked. We conclude that threshold-based models with BCS indicators as predictors are useful to understand disease risk (e.g., anestrus), but conversely, they are useless to predict such multicausal disease events at the herd level.
开展了一项回顾性纵向研究，评估奶牛体况评分（BCS）在个体和畜群水平上对疾病风险的解释和预测能力。收集了来自阿根廷潘帕斯草原两个商业放牧畜群的数据（畜群A = 每年2100头，畜群B = 每年2600头泌乳奶牛），为期4年。使用逻辑模型评估BCS指标与奶牛和畜群水平上发情间期几率的关联。估计人群归因分数（AF）以评估因BCS指标导致的发情间期率。我们发现，BCS≥3且体重减轻≤0.5的奶牛产犊时发情间期风险降低（比值比：0.07 - 0.41），并且BCS适宜的奶牛比例高的群体发情间期率降低（比值比：0.22 - 0.45）。尽管汇总数据具有良好的解释力，但其对畜群水平发情间期率的预测能力较差（曲线下面积：0.574 - 0.679）。两个畜群的AF在研究过程中均有变化，并且每次发情间期率达到峰值时都趋于下降。我们得出结论，以BCS指标作为预测因子的基于阈值的模型有助于理解疾病风险（如发情间期），但相反，它们对于预测畜群水平上此类多因素疾病事件毫无用处。
